摘要
本文在分析碳源排放清单(R_(排放))、生物过程模拟植被净生态系统生产力(NEP_(植被))、大气层碳卫星监测CO_(2)浓度(CO_(2月平均浓度))三种反演时空分布数据的基础上,利用LM最优估算法结合球冠谐模型解析碳的基础量值及空间分布关系,通过计算机深度学习,构建基于上述三个模型融合的大数据模型,以期探明工业碳源排放清单CO_(2)较为明晰准确的反演过程和各个环节的数学关系,为有效的消碳提供技术路径支撑。
Based on the analysis of three kinds of retrieved spatio-temporal distribution data,namely carbon source emission inventory(R emissions),biological process simulation vegetation net ecosystem productivity(NEP vegetation),atmospheric carbon satellite monitoring CO_(2) concentration(monthly average CO_(2) concentration),this paper uses LM optimal estimation algorithm and spherical cap harmonic model to analyze the basic value and spatial distribution relationship of carbon,and through computer indepth learning,Build a big data model based on the fusion of the three models mentioned above,in order to explore the clear and accurate inversion process of industrial carbon source emission inventory CO_(2) and the mathematical relationships of each link,and provide technical path support for effective carbon reduction.
作者
谢优平
肖祥红
蔡观
谢承志
谭思源
Xie Youping;Xiao Xianghong;Cai Guan;Xie Chengzhi;Tan Siyuan(The Second Surveying and Mapping Institute of Hunan Province,Changsha Hunan 410119;Shanghai University of Electric Power,Shanghai 201306)
出处
《国土资源导刊》
2023年第2期93-100,共8页
Land & Resources Herald
基金
2022重大科技研究项目《湖南省自然资源领域推进碳达峰碳中和重大技术研究》(编号:湘自科[2022]5号)。
关键词
相关性分析
量值关系
球冠谐模型
模型融合
LM最优估算法
消碳与控碳
correlation analysis
quantity value relationship
spherical cap harmonic model
model fusion
LM optimal estimation algorithm
carbon reduction and control
